We consider stochastic programs where the distribution of the uncertain parameters is only
observable through a finite training dataset. Using the Wasserstein metric, we construct a …

The objective of uncertainty quantification is to certify that a given physical, engineering or
economic system satisfies multiple safety conditions with high probability. A more ambitious …

We study joint chance constraints where the distribution of the uncertain parameters is only
known to belong to an ambiguity set characterized by the mean and support of the …

Traditional stochastic programs assume that the probability distribution of uncertainty is
known. However, in practice, the probability distribution oftentimes is not known or cannot be …

In optimization problems appearing in fields such as economics, finance, or engineering, it is
often important that a risk measure of a decision-dependent random variable stays below a …

We investigate a class of fractional distributionally robust optimization problems with
uncertain probabilities. They consist in the maximization of ambiguous fractional functions …

We study scheduling problems on unrelated parallel machines with uncertainty in job
processing and sequence-dependent setup times. We first formulate this problem as a two …

Chance constraints ensure the satisfaction of constraints under uncertainty with a desired
probability. This scheme is unfortunately sensitive to assumptions of the probability …

In this paper, we study a distributionally robust optimization approach to chance-constrained
stochastic programs to hedge against uncertainty in the distributions of the random …

The dissertation examines the problem of machine scheduling in an uncertain environment.
To address this challenge, the study employs both traditional operations research (OR) …